scholarly journals Adaptive Neural Control for a Class of Outputs Time-Delay Nonlinear Systems

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Ruliang Wang ◽  
Jie Li
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Nonlinear Systems ◽  
Neural Control ◽  
Closed Loop ◽  
Continuous Functions ◽  
Rbf Neural Networks ◽  
Adaptive Neural Control ◽  
Control Scheme ◽  
Simulation Results

This paper considers an adaptive neural control for a class of outputs time-delay nonlinear systems with perturbed or no. Based on RBF neural networks, the radius basis function (RBF) neural networks is employed to estimate the unknown continuous functions. The proposed control guarantees that all closed-loop signals remain bounded. The simulation results demonstrate the effectiveness of the proposed control scheme.

scholarly journals The Adaptive Neural Control for a Class of High-Order Uncertain Stochastic Nonlinear Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Xiaoyan Qin
Keyword(s):  
Nonlinear Systems ◽  
Neural Control ◽  
Closed Loop ◽  
Control Method ◽  
High Order ◽  
Stochastic Nonlinear Systems ◽  
Closed Loop System ◽  
Adaptive Neural Control ◽  
Control Scheme ◽  
Approximation Capability

This paper studies the problem of the adaptive neural control for a class of high-order uncertain stochastic nonlinear systems. By using some techniques such as the backstepping recursive technique, Young’s inequality, and approximation capability, a novel adaptive neural control scheme is constructed. The proposed control method can guarantee that the signals of the closed-loop system are bounded in probability, and only one parameter needs to be updated online. One example is given to show the effectiveness of the proposed control method.

PREDICTIVE CONTROL OF NONLINEAR SYSTEMS BASED ON IDENTIFICATION BY BACKPROPAGATION NETWORKS

1994 ◽  
Vol 05 (04) ◽  
pp. 335-344 ◽  
Author(s):  
JIANBIN HAO ◽  
JOOS VANDEWALLE ◽  
SHAOHUA TAN
Keyword(s):  
Nonlinear Systems ◽  
Predictive Control ◽  
Neural Control ◽  
Nonlinear Dynamical Systems ◽  
Universal Approximation ◽  
Backpropagation Algorithm ◽  
Nonlinear Dynamical ◽  
Control Scheme ◽  
One Step ◽  
Simulation Results

Using the property of universal approximation of multilayer perceptron neural network, a class of discrete nonlinear dynamical systems are modeled by a perceptron with two hidden layers. A backpropagation algorithm is then used to train the model to identify the nonlinear systems to a desired level of accuracy. Based on the identified model, a one-step-ahead predictive control scheme is proposed in which the future control inputs are obtained through some nonlinear optimization process. Making use of the online learning properties of neural networks, the predictive control scheme is further developed into an adaptive one which is robust to the incompleteness of identification. Simulation results show that this neural control scheme works well even for some very complicated nonlinear systems.

scholarly journals Stem Control of a Sliding-Stem Pneumatic Control Valve Using a Recurrent Neural Network

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Mohammad Heidari ◽  
Hadi Homaei
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Neural Control ◽  
Closed Loop ◽  
Control Valve ◽  
Superior Performance ◽  
Pneumatic Control ◽  
Response Characteristics ◽  
Control Scheme ◽  
Simulation Results

This paper presents a neural scheme for controlling an actuator of pneumatic control valve system. Bondgraph method has been used to model the actuator of control valve, in order to compare the response characteristics of valve. The proposed controller is such that the system is always operating in a closed loop, which should lead to better performance characteristics. For comparison, minimum- and full-order observer controllers are also utilized to control the actuator of pneumatic control valve. Simulation results give superior performance of the proposed neural control scheme.

Adaptive neural control for a class of output feedback time delay nonlinear systems

2009 ◽  
Vol 72 (7-9) ◽  
pp. 1985-1992 ◽  
Author(s):  
Qing Zhu ◽  
Tianping Zhang ◽  
Shumin Fei ◽  
Kanjian Zhang ◽  
Tao Li
Keyword(s):  
Time Delay ◽  
Nonlinear Systems ◽  
Output Feedback ◽  
Neural Control ◽  
Adaptive Neural Control ◽  
Feedback Time

Adaptive RBF neural-networks control for a class of time-delay nonlinear systems

2008 ◽  
Vol 71 (16-18) ◽  
pp. 3617-3624 ◽  
Author(s):  
Qing Zhu ◽  
Shumin Fei ◽  
Tianping Zhang ◽  
Tao Li
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Nonlinear Systems ◽  
Rbf Neural Networks
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